Automated strategy searches in an electronic goods market: Learning and complex price schedules (1999)
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| Venue: | In Proceedings of ACM EC-99 |
| Citations: | 15 - 6 self |
BibTeX
@INPROCEEDINGS{Brooks99automatedstrategy,
author = {Christopher H. Brooks and Scott Fay and Rajarshi Das and Jeffrey K. MacKie-Mason and Jeffrey O. Kephart and Edmund H. Durfee},
title = {Automated strategy searches in an electronic goods market: Learning and complex price schedules},
booktitle = {In Proceedings of ACM EC-99},
year = {1999},
pages = {31--40},
publisher = {ACM Press}
}
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Abstract
In an automated market for electronic goods problems arise that have not been well studied previously. For example, information goods are very flexible. In contrast to physical goods, marginal costs are negligible and nearly limitless bundling and unbundling of these items are possible. Consequently, producers can offer complex pricing schemes. However, the profit-maximizing design of a complex pricing schedule depends on a producer's knowledge of the distribution of consumer preferences for the available information goods. Preferences are private and can only be gradually uncovered through market experience. In this paper we compare dynamic performance across price schedules of varying complexity. We provide the producer with two machine learning
